VitaGraph is presented, a comprehensive multi-purpose biological knowledge graph built by integrating and refining multiple public datasets and enabling benchmarking of graph-based models and offering the opportunity to tackle tasks such as drug repurposing, PPI prediction, and side-effect prediction, among others.
Abstract
The complexity of human biology poses ongoing challenges, driving global interdisciplinary research. Artificial intelligence has become a powerful tool in computational biology, where graph data structures model entities like protein–protein interaction (PPI) networks and gene functional networks. These networks support crucial tasks in network medicine, including gene–disease association prediction, drug repurposing, and polypharmacy side-effect analysis. Reliable machine learning predictions require high-quality data. We present VitaGraph, a comprehensive multi-purpose biological knowledge graph built by integrating and refining multiple public datasets. Extending the Drug Repurposing Knowledge Graph, our pipeline: (a) resolves inconsistencies and redundancies, (b) consolidates information from leading public sources, and (c) enriches graph nodes with expressive features such as molecular fingerprints and gene ontologies. Incorporating biologically and chemically meaningful features enhances machine learning models’ ability to learn accurate, structured embedding spaces. The resulting resource offers a coherent, reliable platform to advance computational biology and precision medicine while enabling benchmarking of graph-based models and offering the opportunity to tackle tasks such as drug repurposing, PPI prediction, and side-effect prediction, among others.
SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.
Bowei Zhao, Hui Zhao, Yu-an Huang et al.· IEEE transactions on computa...· 0 citations
Drug repurposing represents a cost-effective strategy to identify novel therapeutic applications for existing pharmaceuticals, circumventing the protracted timelines of traditional drug discovery. While knowledge graph (KG) based methods excel at integrating heterogeneous biomedical data, they often struggle to harmonize high-level domain knowledge with fine-grained molecular mechanisms. We propose KGDDA, a multimodal framework designed for drug-disease association prediction that synergistically integrates KGs with medical ontologies. By leveraging an attention-driven fusion mechanism, KGDDA dynamically merges contextual topological embeddings with ontology-derived priors, enabling the adaptive capture of intricate drug-disease interactions. Extensive evaluations on two benchmark datasets demonstrate that KGDDA consistently outperforms state-of-the-art baselines in both predictive accuracy and generalization. Furthermore, case studies on head and neck cancer and small cell lung cancer validate KGDDA's ability to provide actionable mechanistic insights, highlighting its potential to accelerate therapeutic discovery and precision medicine.
Qichang Zhao, Qiao Ling, Muhammad Habibulla Alamin et al.· IEEE transactions on computa...· 0 citations
Accurately predicting disease–protein relations in biomedical knowledge graphs helps link disease phenotypes to molecular mechanisms and supports disease-related knowledge discovery and candidate target identification. Biomedical knowledge graphs organize multisource biomedical knowledge, including diseases, proteins, drugs, and pathways, as entity nodes and relational edges, providing a structured foundation for modeling complex biomedical associations. Existing methods are often constrained by single-modality modeling, shallow graph–text fusion, and insufficient traceable evidence, which limits their ability to exploit graph–text complementarity and weakens downstream validation and structural evidence interpretation.
To address these limitations, we propose NEOGRAN, a graph–text collaborative framework comprising three core modules for relation prediction in biomedical knowledge graphs. The dual-encoder architecture captures graph structural patterns and biomedical entity representations to mitigate single-modality modeling. The bidirectional cross-attention module enables deep graph–text interaction to overcome shallow fusion. The interpretable path module generates traceable evidence paths to support prediction verification, structural evidence interpretation, and hypothesis generation. On PrimeKG, NEOGRAN achieved an AUPR of 0.9860 and an AUROC of 0.9875 under the 1:1 sampled classification setting, and further obtained an MRR of 0.0735 in the all-candidate ranking evaluation. External validation on BioKG further shows that NEOGRAN remains effective under differences in entity coverage, relation composition, and local topology, supporting its method-level generalizability across knowledge graph sources.
NEOGRAN provides an effective solution for relation prediction in biomedical knowledge graphs while offering traceable structural evidence for hypothesis generation and further biological validation.
This work proposes an enhanced learning framework that deeply integrates structured logical knowledge within GNN models, and demonstrates that incorporating domain-specific relational knowledge leads to better generalization and robustness compared to standard GNNs.
Kai Hodžić, Gustav Šír· ACM Transactions on Intellig...· 0 citations
Biomedical knowledge extraction is fundamental to understanding complex biological systems, yet they remain challenging due to the rapid growth of biomedical literature, the heterogeneity of data sources, and the limitations of traditional rule-based and supervised learning approaches. Large language models (LLMs) have shown considerable promise for biomedical knowledge retrieval and discovery by demonstrating strong capacities for contextual reasoning and information extraction; however, their reliability, robustness, and interpretability in biomedical applications remain insufficiently understood. In particular, systematic evaluations against curated biological ground truth, multimodal pathway analysis, and scalable prompt optimization have received limited attention. This work presents a comprehensive investigation of LLMs for biomedical knowledge extraction, with a focus on gene–gene regulatory relation prediction, pathway component recognition, multimodal pathway figure understanding, and automated prompt optimization. First, a large-scale benchmarking study evaluates multiple proprietary and open-source LLMs using curated ground-truth data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), which reveals substantial variability across models and persistent challenges in recall, directional inference, and hallucination control. Second, this work extends biomedical knowledge extraction beyond text by introducing a Genetic Prompt Vision framework for multimodal pathway image analysis. This framework employs evolutionary optimization to automatically generate effective multimodal prompts for extracting gene–gene interactions from biological pathway figures. Experimental results demonstrate that vision–language models enhanced through automated prompt optimization outperform traditional OCR-based pipelines in extraction accuracy and biological interpretability. Third, this study proposes a Bayesian Optimization Prompt framework for automated prompt optimization in biomedical relation extraction. This model-agnostic approach identifies robust prompts of ASCII strings without accessing model internals or performing fine-tuning. Across multiple LLMs, automated prompt optimization improves predictive performance while enhancing internal representation quality, as evidenced by improved semantic clustering and representation stability. Overall, this work establishes a scalable, reproducible framework for evaluating, optimizing, and interpreting LLMs for biomedical knowledge extraction. The findings provide practical insights into the strengths and limitations of LLMs in biomedical contexts and contribute methodological advances toward a reliable, interpretable, and multimodal LLM-based bioinformatics system.